关键词: 段落相似度, 句子主干提取, 二部图匹配, 向量空间模型, 中文分词

Abstract: In order to improve the low accuracy of the statistical method that is represented by the traditional Vector Space Model （VSM） and based on word frequency in Chinese paragraph similarity computing, this thesis proposes a method to compute Chinese paragraph similarity on the basis of weighted bipartite graph matching. The similarity computing method will be divided into two levels：paragraphs and sentences. Thus, sentences can be treated as paragraphs and calculated the similarity by using bipartite graph matching. First of all, it utilizes key words extraction algorithm to extract the main vocabulary backbone of the sentences, using the main vocabulary as vertex of weighted bipartite graph to calculate similarity of sentences. Secondly, it calculates the paragraph similarity by using the sentence as a vertex of weighted bipartite graph, and the similarity between sentences as the weight coefficient between the vertex of weighted bipartite graph. Experimental results show that the proposed method has been greatly increased in accuracy compared with VSM, in virtue of its ability to identify synonyms accurately and match two similar words in different locations of paragraphs automatically.

Key words: paragraph similarity, sentence keywords, bipartite graph match, vector space model, participle